Assessment of the real‐time pattern recognition capability of machine learning algorithms.
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| Title: | Assessment of the real‐time pattern recognition capability of machine learning algorithms. |
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| Authors: | Polytarchos, Elias1 (AUTHOR) ipoli@aueb.gr, Bardaki, Cleopatra2 (AUTHOR), Pramatari, Katerina1 (AUTHOR) |
| Source: | Statistical Analysis & Data Mining. Jun2024, Vol. 17 Issue 3, p1-15. 15p. |
| Subjects: | Pattern recognition systems, John Wiley & Sons Inc., Machine learning, Blockchains, Internet of things |
| Abstract: | Nowadays data streams from different sources, like blockchain‐based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize patterns in real time from these streams, while adapting to their velocity and veracity, is emerging. Established machine learning algorithms used for pattern recognition methods have not been designed taking under account the volume, velocity, diversity, and accuracy of the data streams. This research contributes with an approach for assessing the pattern recognition capabilities of established machine learning algorithms when handling volatile data in real time and proposes a system that adapts the algorithms to the requirements of data streams, as well as assesses their pattern recognition capabilities based on established criteria. The system was applied for assessing five machine learning algorithms with input from a data stream from Bluetooth beacons tracking consumers in a retail store. This research can support future data scientists and analysts who need to reveal data patterns in big, volatile data streams in real time in order to support effective decision‐making in the respective application domain. Copyright © 2024 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
| Copyright of Statistical Analysis & Data Mining is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Nowadays data streams from different sources, like blockchain‐based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize patterns in real time from these streams, while adapting to their velocity and veracity, is emerging. Established machine learning algorithms used for pattern recognition methods have not been designed taking under account the volume, velocity, diversity, and accuracy of the data streams. This research contributes with an approach for assessing the pattern recognition capabilities of established machine learning algorithms when handling volatile data in real time and proposes a system that adapts the algorithms to the requirements of data streams, as well as assesses their pattern recognition capabilities based on established criteria. The system was applied for assessing five machine learning algorithms with input from a data stream from Bluetooth beacons tracking consumers in a retail store. This research can support future data scientists and analysts who need to reveal data patterns in big, volatile data streams in real time in order to support effective decision‐making in the respective application domain. Copyright © 2024 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19321864 |
| DOI: | 10.1002/sam.11701 |
